International audienceIn some recent works, model-based filtering approaches have been proved as effective methods for extracting ECG signals from single channel noisy recordings. The previously developed methods, use a highly realistic nonlinear ECG model for the construction of Bayesian filters. In this work, a multi-channel extension of the previous approach is developed, by using a three dimensional model of the cardiac dipole vector. The results have considerable improvement compared with the single channel approach. The method is hence believed to be applicable to low SNR multi-channel recordings
With an increasing focus on automatic diagnoses of cardiac disease through ECG signals, de-noising t...
International audienceIn this paper, we introduce a model-based Bayesian denoising framework for pho...
Electrocardiogram (ECG) is required during Magnetic Resonance Imaging (MRI) for two reasons, patient...
International audienceIn this paper a nonlinear Bayesian filtering framework is proposed for the fil...
Abstract. Electrocardiogram and magnetocardiogram signals are among the most considerable sources of...
Electrocardiogram (ECG) and magnetocardiogram (MCG) signals are among the most considerable sources ...
International audienceElectrocardiogram (ECG) and magnetocardiogram (MCG) signals are among the most...
Abstract—In this paper, a nonlinear Bayesian filtering frame-work is proposed for the filtering of s...
The electrocardiogram (ECG) denoising is of paramount importance for accurate disease diagnosis, but...
Abstract—In this paper a nonlinear Bayesian filtering frame-work is proposed for the filtering of si...
View references (47) The ECG filtering problem is a widely explored research topic. In this paper...
View references (47) The ECG filtering problem is a widely explored research topic. In this paper...
View references (47) The ECG filtering problem is a widely explored research topic. In this paper...
In this paper, we describe a Gaussian wave-based state space to model the temporal dynamics of elect...
In this paper, we describe a Gaussian wave-based state space to model the temporal dynamics of elect...
With an increasing focus on automatic diagnoses of cardiac disease through ECG signals, de-noising t...
International audienceIn this paper, we introduce a model-based Bayesian denoising framework for pho...
Electrocardiogram (ECG) is required during Magnetic Resonance Imaging (MRI) for two reasons, patient...
International audienceIn this paper a nonlinear Bayesian filtering framework is proposed for the fil...
Abstract. Electrocardiogram and magnetocardiogram signals are among the most considerable sources of...
Electrocardiogram (ECG) and magnetocardiogram (MCG) signals are among the most considerable sources ...
International audienceElectrocardiogram (ECG) and magnetocardiogram (MCG) signals are among the most...
Abstract—In this paper, a nonlinear Bayesian filtering frame-work is proposed for the filtering of s...
The electrocardiogram (ECG) denoising is of paramount importance for accurate disease diagnosis, but...
Abstract—In this paper a nonlinear Bayesian filtering frame-work is proposed for the filtering of si...
View references (47) The ECG filtering problem is a widely explored research topic. In this paper...
View references (47) The ECG filtering problem is a widely explored research topic. In this paper...
View references (47) The ECG filtering problem is a widely explored research topic. In this paper...
In this paper, we describe a Gaussian wave-based state space to model the temporal dynamics of elect...
In this paper, we describe a Gaussian wave-based state space to model the temporal dynamics of elect...
With an increasing focus on automatic diagnoses of cardiac disease through ECG signals, de-noising t...
International audienceIn this paper, we introduce a model-based Bayesian denoising framework for pho...
Electrocardiogram (ECG) is required during Magnetic Resonance Imaging (MRI) for two reasons, patient...